AI & ML Empowerment

Cultivating Conceptual Understanding of How AI/ML Methods work Under the Hood

FOCUS QUESTIONS

Pattern

How can the technical concepts underpinning AI & Machine Learning (ML) be made intuitive and understandable for entrepreneurs and educators from diverse disciplines and backgrounds?

In what ways can we make learning technical AI & ML concepts inclusive and engaging through cutting-edge educational technology?

How can we empower entrepreneurs and educators to understand how and why AI & ML tools and capabilities work?

IMPACTS

  • New courses: natural language processing, large language models, computer vision, generative AI

  • Pedagogical innovation through design and development of educational technology

  • Co-curricular workshops supporting direct AI & ML experimentation

  • Cutting edge, data-driven case studies for teaching AI & ML

  • White papers documenting the use of technical AI & ML tools

  • Interdisciplinary applied research using AI & ML

  • Student-built, AI-powered tools and interfaces

  • Foster conceptual, intuitive understanding of AI & ML through speaker series

OUTCOMES

  • Tech talks delivered on campus by AI/ML experts on technical topics such as word2vec, GPT, BERT, and fine-tuning of large language models.
  • Student internships focused on early-stage research in the areas of dream analysis, stress-testing of large language models, and the use of natural language processing for mental health.

LEADER

Davit Khachatryan, Associate Professor, MAST

Davit Khachatryan

Associate Professor, MAST
Leader of The Generator’s AI & ML Empowerment Specialty Lab

“The ML tools underlying AI are technical but can be made intuitive and accessible to users. Gaining an intuitive understanding of what happens under the hood of AI technology will empower humans as AI continues to permeate our daily lives.”

AI research interests

  • AI and Machine Learning
  • AI pedagogy
  • Generative AI

Davit Khachatryan is a data scientist specializing in machine learning and natural language processing. His pedagogical innovations include the development of Playmeans, a web app for inclusive data science education using live data from Spotify; as well as Cases used in teaching time series analysis. Before Babson College, he was a Senior Associate at PricewaterhouseCoopers (PwC), focusing on predictive modeling and advanced data analytics for clients in healthcare, finance, and government sectors. At Babson, he teaches courses in machine learning, data science, and statistics.

Recent publications

Girdharry, K., & Khachatryan, D. (2024). Meaningful Writing in the Age of Generative Artificial Intelligence. Double Helix, 11.

Khachatryan, D. (2023). Playmeans: Inclusive and Engaging Data Science through Music. Journal of Statistics and Data Science Education, 31(2), 151-161.

Eloyan, A., Yue, M. S., & Khachatryan, D. (2020). Tumor heterogeneity estimation for radiomics in cancer. Statistics in medicine, 39(30), 4704-4723.

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